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• W4309652835 abstract "Abstract Objective: Predictors of one-year mortality for elderly patients referred to intensive care units (ICUs) with chronic obstructive pulmonary disease (COPD) remain poorly described. We sought to create and verify a model for predicting all-cause one-year death among COPD patients admitted to the ICU. Methods: A retrospective cohort study was designed. The Medical Information Mart for Intensive Care (MIMIC-IV) database was mined for information. A total of 2313 patients with COPD who met the inclusion criteria were screened for eligibility using registry data from the MIMIC-IV database between January 2008 and December 2019, which was randomized into training (n = 1628, 70%) and testing groups (n = 685, 30%). The tree model-based extreme gradient boosting (XGBoost), random forest (RF), and lightGBM algorithms were used in the training samples to rank feature importance and screen out its top 10 union features as independent mortality risk factors.The variance inflation factor (VIF) was used to check for collinearity in the prediction model's input variables, and multivariate logistic regression analysis was used to take a gander at factors related to mortality and then to use those factors to create a nomogram for predicting prognosis, which was then tested on the testing cohort. The AUC, calibration plot, and decision curve analysis were performed to evaluate the predictive model's discrimination, calibration, and clinical utility. Results: The results showed that ∼40.60% (939 out of 2,313) of the patients died. In multivariate logistic regression analyses, weight, BUN, RDW, MCHC, age, malignancy, and heart_rate were all independent predictors of mortality. In both groups, the nomogram showed acceptable discrimination (area under curve [AUC] = 0.7216 [95% confidence interval (CI) 0.6968–0.7465] and AUC=0.7481 [95% CI 0.7116–0.7845], respectively) and good calibration compared to SOFA, SAPS II, LODS, APS III, OASIS, SIRS, GCS, and MELD risk scores.In addition, it was discovered that calibration plots accurately predicted one-year mortality. The examination of decision curves (DCAs) revealed that the nomogram model had a greater net benefit. Conclusions: Our nomogram provides interpretable and visual explanations of customized one-year death projections in ICU-admitted elderly patients with COPD, which may aid clinical clinicians in comprehending the effects of key model characteristics and the model's decision-making for such patients." @default.
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• W4309652835 date "2022-11-22" @default.
• W4309652835 modified "2023-09-30" @default.
• W4309652835 title "Development of a machine learning-based nomogram for predicting one-year mortality in elderly patients with chronic obstructive pulmonary disease in intensive care unit" @default.
• W4309652835 doi "https://doi.org/10.21203/rs.3.rs-2282103/v1" @default.
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